code: def frobenius(
      detection: "Detection", tracked_object: "TrackedObject"
  ) -> float:
      """
      Frobernius norm on the difference of the points in detection and the estimates in tracked_object.
  
      The Frobenius distance and norm are given by:
  
      $$
      d_f(a, b) = ||a - b||_F
      $$
  
      $$
      ||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}
      $$
  
      Parameters
      ----------
      detection : Detection
          A detection.
      tracked_object : TrackedObject
          A tracked object.
  
      Returns
      -------
      float
          The distance.
  
      See Also
      --------
      [`np.linalg.norm`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html)
      """
      return np.linalg.norm(detection.points - tracked_object.estimate)
  
imports: from abc import ABC, abstractmethod
  from functools import partial
  from logging import warning
  from typing import TYPE_CHECKING, Callable, List, Optional, Sequence, Union
  import numpy
  from scipy.spatial.distance import cdist
label_desc: Use image node_id 1 for calling a global function with example usage: frobenius(detection, tracked_object) and returns: np